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63_conv_standard_2D__square_input__square_kernel • adaptive_conv2d_cuda_base

Level 1 • Task 63
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor,
    stride: int,
    padding: int,
    dilation: int,
    groups: int,
) -> torch.Tensor:
    """
    Performs a standard 2D convolution operation with a square input and square kernel.

    Args:
        x (torch.Tensor): Input tensor.
        weight (torch.Tensor): Weight tensor.
        bias (torch.Tensor): Bias tensor.
        stride (int): Stride of the convolution.
        padding (int): Padding applied to the input.
        dilation (int): Dilation of the convolution.
        groups (int): Number of blocked connections from input channels to output channels.

    Returns:
        torch.Tensor: Output tensor.
    """
    return F.conv2d(
        x,
        weight,
        bias,
        stride=stride,
        padding=padding,
        dilation=dilation,
        groups=groups,
    )


class Model(nn.Module):
    """
    Performs a standard 2D convolution operation with a square input and square kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (int): Size of the square convolution kernel.
        stride (int): Stride of the convolution.
        padding (int): Padding applied to the input.
        dilation (int): Spacing between kernel elements.
        groups (int): Number of blocked connections from input channels to output channels.
        bias (bool): If `True`, adds a learnable bias to the output.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int,
        padding: int,
        dilation: int,
        groups: int,
        bias: bool,
    ):
        super(Model, self).__init__()
        # Create a Conv2d layer to get the same initialization
        conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )
        # Copy the initialized parameters
        self.weight = nn.Parameter(conv.weight.clone())
        self.bias = nn.Parameter(conv.bias.clone()) if bias else None

        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups

    def forward(
        self,
        x: torch.Tensor,
        fn=module_fn,
    ) -> torch.Tensor:
        """
        Performs the 2D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return fn(
            x,
            self.weight,
            self.bias,
            self.stride,
            self.padding,
            self.dilation,
            self.groups,
        )


# Constants
batch_size = 16
in_channels = 3
out_channels = 64
kernel_size = 3
width = 256
height = 256
stride = 1
padding = 0
dilation = 1
groups = 1
bias = False


def get_inputs():
    x = torch.randn(batch_size, in_channels, height, width)
    return [x]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        groups,
        bias,
    ]

import torch
import torch.nn as nn


class Model(nn.Module):
    """
    Performs a standard 2D convolution operation with a square input and square kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (int): Size of the square convolution kernel.
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int, optional): Padding applied to the input. Defaults to 0.
        dilation (int, optional): Spacing between kernel elements. Defaults to 1.
        groups (int, optional): Number of blocked connections from input channels to output channels. Defaults to 1.
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        stride: int = 1,
        padding: int = 0,
        dilation: int = 1,
        groups: int = 1,
        bias: bool = False,
    ):
        super(Model, self).__init__()
        self.conv2d = nn.Conv2d(
            in_channels,
            out_channels,
            (kernel_size, kernel_size),
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 2D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return self.conv2d(x)


# Test code
batch_size = 16
in_channels = 3
out_channels = 64
kernel_size = 3
width = 256
height = 256
stride = 1
padding = 0
dilation = 1
groups = 1
bias = False


def get_inputs():
    x = torch.randn(batch_size, in_channels, height, width)
    return [x]


def get_init_inputs():
    return [
        in_channels,
        out_channels,
        kernel_size,
        stride,
        padding,
        dilation,
        groups,
        bias,
    ]  # Provide in_channels, out_channels, kernel_size for initialization
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 63_conv_standard_2D__square_input__square_kernel
Level ID 1
Task ID 63
Kernel Name adaptive_conv2d_cuda_base
CUDA Speedup (Native) 0.998x
CUDA Speedup (Compile) 1.676x
CUDA Runtime 0.230 ms
PyTorch Runtime (Native) 0.230 ms
PyTorch Runtime (Compile) 0.385 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
Temperature 0.50
View Experiment Progress Details

Related Kernels (Level 1, Task 63 • 63_conv_standard_2D__square_input__square_kernel)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 63_conv_standard_2D__square_input__square_kernel 0.23 1.00 1.68
🥇 adaptive_conv2d_cuda_base 0.23 1.00 1.68
🥇 conv2d_minimized_warp_divergence_base 0.23 1.00 1.68
🥇 adaptive_conv2d_cuda_base 0.23 1.00 1.68
5 conv2d_shared_mem_optimized_base 0.43 0.54 0.90
6 conv2d_coalesced_coalescing_base 0.85 0.27 0.45
7 conv2d_shared_mem_optimized_base 1.10 0.21 0.35
8 conv2d_shared_mem_optimized_base 1.10 0.21 0.35
8 conv2d_shared_mem_opt_base_base 1.10 0.21 0.35
10 63_conv_warp_optimized_base 1.18 0.19 0.33
11 mod_conv2d_kernel_modular_base 1.20 0.19 0.32
12 conv2d_unrolled_shared_base 1.22 0.19 0.32
13 63_conv_optimized_thread_mapping_base 1.34 0.17 0.29
14 constant_memory_optim_conv2d_edit_1 1.35 0.17 0.28
15 conv2d_shared_atomic_minimized_base 1.39 0.17 0.28
16 conv2d_grid_stride_base 1.41 0.16 0.27
17 atomic_minimized_conv2d_base_base 1.42 0.16 0.27
18 balanced_conv2d_cuda_base 1.44 0.16 0.27
19 block_size_optimization_conv2d_base 1.45 0.16 0.27
20 block_size_optimization_conv2d_edit_1 1.47 0.16 0.26 
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>

#define BLOCK_SIZE 16
#define KERNEL_SIZE 3
#define SHARED_SIZE (BLOCK_SIZE + KERNEL_SIZE - 1)
#define MIN_CHANNELS_THRESHOLD 16
#define MIN_SIZE_THRESHOLD 32

__global__ void conv2d_kernel_shared(
    const float* __restrict__ input,
    const float* __restrict__ weight,
    float* __restrict__ output,
    const int batch_size,
    const int in_channels,
    const int out_channels,
    const int input_height,
    const int input_width,
    const int output_height,
    const int output_width,
    const int stride,
    const int padding) {
    __shared__ float shared_input[SHARED_SIZE][SHARED_SIZE];
    __shared__ float shared_weight[KERNEL_SIZE][KERNEL_SIZE];
    
    const int tx = threadIdx.x;
    const int ty = threadIdx.y;
    const int bx = blockIdx.x * BLOCK_SIZE;
    const int by = blockIdx.y * BLOCK_SIZE;
    const int b = blockIdx.z;
    
    const int x = bx + tx;
    const int y = by + ty;
    
    for (int oc = 0; oc < out_channels; ++oc) {
        float sum = 0.0f;
        
        for (int ic = 0; ic < in_channels; ++ic) {
            if (tx < KERNEL_SIZE && ty < KERNEL_SIZE) {
                int weight_idx = ((oc * in_channels + ic) * KERNEL_SIZE + ty) * KERNEL_SIZE + tx;
                shared_weight[ty][tx] = weight[weight_idx];
            }
            __syncthreads();
            
            for (int i = ty; i < SHARED_SIZE; i += BLOCK_SIZE) {
                for (int j = tx; j < SHARED_SIZE; j += BLOCK_SIZE) {
                    int ih = by + i - padding;
                    int iw = bx + j - padding;
                    
                    if (ih >= 0 && ih < input_height && iw >= 0 && iw < input_width) {
                        shared_input[i][j] = input[((b * in_channels + ic) * input_height + ih) * input_width + iw];
                    } else {
                        shared_input[i][j] = 0.0f;
                    }
                }
            }
            __syncthreads();
            
            if (x < output_width && y < output_height) {
                for (int ki = 0; ki < KERNEL_SIZE; ++ki) {
                    for (int kj = 0; kj < KERNEL_SIZE; ++kj) {
                        int sy = ty * stride + ki;
                        int sx = tx * stride + kj;
                        sum += shared_input[sy][sx] * shared_weight[ki][kj];
                    }
                }
            }
            __syncthreads();
        }
        
        if (x < output_width && y < output_height) {
            int output_idx = ((b * out_channels + oc) * output_height + y) * output_width + x;
            output[output_idx] = sum;
        }
    }
}

torch::Tensor forward(
    torch::Tensor x,
    torch::Tensor weight,
    torch::optional<torch::Tensor> bias,
    int stride,
    int padding,
    int dilation,
    int groups) {
    
    TORCH_CHECK(x.is_cuda(), "Input must be a CUDA tensor");
    TORCH_CHECK(weight.is_cuda(), "Weight must be a CUDA tensor");
    
    bool use_custom_impl = (x.size(1) >= MIN_CHANNELS_THRESHOLD && 
                          x.size(2) >= MIN_SIZE_THRESHOLD &&
                          x.size(3) >= MIN_SIZE_THRESHOLD &&
                          weight.size(2) == KERNEL_SIZE &&
                          weight.size(3) == KERNEL_SIZE &&
                          dilation == 1 &&
                          groups == 1);
                          
    if (!use_custom_impl) {
        if (bias.has_value()) {
            return torch::conv2d(x, weight, bias.value(), 
                               {stride, stride}, 
                               {padding, padding}, 
                               {dilation, dilation}, 
                               groups);
        } else {
            return torch::conv2d(x, weight, torch::Tensor(), 
                               {stride, stride}, 
                               {padding, padding}, 
                               {dilation, dilation}, 
                               groups);
        }
    }
    
    auto batch_size = x.size(0);
    auto in_channels = x.size(1);
    auto input_height = x.size(2);
    auto input_width = x.size(3);
    auto out_channels = weight.size(0);
    
    auto output_height = (input_height + 2 * padding - KERNEL_SIZE) / stride + 1;
    auto output_width = (input_width + 2 * padding - KERNEL_SIZE) / stride + 1;
    
    auto output = torch::empty({batch_size, out_channels, output_height, output_width},
                             x.options());
    
    dim3 threads(BLOCK_SIZE, BLOCK_SIZE);
    dim3 blocks((output_width + BLOCK_SIZE - 1) / BLOCK_SIZE,
                (output_height + BLOCK_SIZE - 1) / BLOCK_SIZE,
                batch_size);
    
    conv2d_kernel_shared<<<blocks, threads>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        output.data_ptr<float>(),
        batch_size,
        in_channels,
        out_channels,
        input_height,
        input_width,
        output_height,
        output_width,
        stride,
        padding);
    
    if (bias.has_value()) {
        output.add_(bias.value().view({1, -1, 1, 1}));
    }
    
    return output;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
    m.def("forward", &forward, "Adaptive CUDA forward function for 2D convolution");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::conv2d
CPU Time 541870.82 μs
Device Time 1453610.44 μs
Self CPU Time 10219.43 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::convolution
CPU Time 531651.39 μs
Device Time 1453610.44 μs
Self CPU Time 11801.10 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_convolution
CPU Time 519850.29 μs
Device Time 1453610.44 μs
Self CPU Time 14589.47 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::cudnn_convolution
CPU Time 505260.83 μs
Device Time 1453610.44 μs
Self CPU Time 149735.46 μs
Self Device Time 1453610.44 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm80_xmma_fprop_implicit_gemm_f32f32_f32f32_f32_nchwkcrs_nchw_tilesize256x64x8_stage3_warpsize2x2x1_g1_ffma_aligna4_alignc4_execute_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 1453607.72 μs
Self CPU Time 0.00 μs
Self Device Time 1453607.72 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::zero_
CPU Time 1328179.02 μs
Device Time 501033.70 μs
Self CPU Time 13365.24 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::fill_
CPU Time 1314815.59 μs
Device Time 501033.70 μs
Self CPU Time 17982.63 μs
Self Device Time 501033.70 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
cudaLaunchKernel
CPU Time 1296832.96 μs
Device Time 0.00 μs
Self CPU Time 1296832.96 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45301 warnings generated when compiling for host.
Suppressed 45327 warnings (45280 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:12:5 bugprone-easily-swappable-parameters
12 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
13 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:12:31: note: the first parameter in the range is 'input'
12 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:13:31: note: the last parameter in the range is 'weight'
13 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:15:5: warning: 3 adjacent parameters of 'conv2d_kernel_shared' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | const int batch_size,
| ^~~~~~~~~~~~~~~~~~~~~
16 | const int in_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
17 | const int out_channels,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:15:15: note: the first parameter in the range is 'batch_size'
15 | const int batch_size,
| ^~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:17:15: note: the last parameter in the range is 'out_channels'
17 | const int out_channels,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:19:5: warning: 2 adjacent parameters of 'conv2d_kernel_shared' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
19 | const int input_width,
| ^~~~~~~~~~~~~~~~~~~~~~
20 | const int output_height,
| ~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:19:15: note: the first parameter in the range is 'input_width'
19 | const int input_width,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:20:15: note: the last parameter in the range is 'output_height'
20 | const int output_height,
| ^~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:21:5: warning: 3 adjacent parameters of 'conv2d_kernel_shared' of similar type ('const int') are easily swapped by mistake [bugprone-easily-swappable-parameters]
21 | const int output_width,
| ^~~~~~~~~~~~~~~~~~~~~~~
22 | const int stride,
| ~~~~~~~~~~~~~~~~~
23 | const int padding) {
| ~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:21:15: note: the first parameter in the range is 'output_width'
21 | const int output_width,
| ^~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:23:15: note: the last parameter in the range is 'padding'
23 | const int padding) {
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:27:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
27 | const int tx = threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:28:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
28 | const int ty = threadIdx.y;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:29:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
29 | const int bx = blockIdx.x * BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:30:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
30 | const int by = blockIdx.y * BLOCK_SIZE;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:31:19: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
31 | const int b = blockIdx.z;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:80:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
80 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:81:19: warning: the parameter 'weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
81 | torch::Tensor weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:101:45: warning: conversion from 'std::optional<at::Tensor>' into 'at::Tensor' and back into 'std::optional<at::Tensor>', remove potentially error-prone optional dereference [bugprone-optional-value-conversion]
101 | return torch::conv2d(x, weight, bias.value(),
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:101:50: note: remove call to 'value' to silence this warning
101 | return torch::conv2d(x, weight, bias.value(),
| ~^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:121:42: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
121 | auto output_height = (input_height + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:121:42: note: make conversion explicit to silence this warning
4 | auto output_height = (input_height + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:121:42: note: perform multiplication in a wider type
121 | auto output_height = (input_height + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:122:40: warning: performing an implicit widening conversion to type 'int64_t' (aka 'long') of a multiplication performed in type 'int' [bugprone-implicit-widening-of-multiplication-result]
122 | auto output_width = (input_width + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:122:40: note: make conversion explicit to silence this warning
122 | auto output_width = (input_width + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^~~~~~~~~~~
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:122:40: note: perform multiplication in a wider type
122 | auto output_width = (input_width + 2 * padding - KERNEL_SIZE) / stride + 1;
| ^
| static_cast<int64_t>( )
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:136:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
136 | batch_size,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:137:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
137 | in_channels,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:138:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
138 | out_channels,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:139:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
139 | input_height,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:140:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
140 | input_width,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:141:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
141 | output_height,
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250202_optimize_b10_s4_e0_sweep/level_1/task_63/b8_s2_adaptive_conv2d_cuda/base/base.cu:142:9: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
142 | output_width,
| ^